Amor

Amor is a precision recruiting platform that redefines how technical talent is discovered. In a hiring landscape saturated with curated LinkedIn profiles, Amor provides a definitive, code-first alternative. It is designed for engineering managers, recruiters, and sourcers who prioritize verifiable technical skill over self-reported experience. The platform analyzes the actual work of over 8 million software engineers by tracking their GitHub activity, including contributions, commit patterns, and project involvement. This data-driven approach surfaces the top 1% of engineers who are consistently building and contributing to meaningful projects. Amor answers highly specific, technical sourcing queries in seconds—such as finding front-end experts in a specific city who contribute to key open-source frameworks—replacing hours of manual script-writing and guesswork. By focusing on cleaned, actionable data from GitHub, Amor enables teams to access a vast, often untapped pool of elite talent that prioritizes building over networking, ultimately helping companies hire better engineers faster.

Top 1% Engineer Discovery

Amor's core intelligence tracks over 100,000 engineers with more than 500 contributions in the last year, algorithmically identifying the most active and proficient builders. By analyzing a dataset of 8 million+ profiles and 145 billion+ stars, the platform filters out noise to surface only the most consistently engaged technical talent, ensuring you evaluate candidates who are genuinely at the top of their field.

Contribution Pattern Analysis

Move beyond simple activity metrics. Amor provides deep insights into a developer's work ethic and habits by analyzing their coding frequency, consistency, and even weekend activity. This allows hiring teams to assess cultural fit and dedication, identifying engineers whose contribution patterns align with your team's expectations and workflow.

Smart Location Filtering

GitHub location data is notoriously noisy and unreliable. Amor cleans this data at scale, enabling precise searches by actual city, region, and country. This feature eliminates the frustration of manual location verification, allowing recruiters to build accurate, geographically targeted talent pipelines with confidence.

Repository Insights & Profile Summaries

Quickly understand a developer's technical interests and project focus. Amor analyzes the types of repositories they build and star, generating an automatic, easy-to-skim summary of their skills and passions. This saves invaluable time per profile, allowing you to assess technical alignment at a glance without deep-diving into countless repos.

For In-House Recruiting Teams

Scale your engineering hiring efficiently without compromising on quality. In-house recruiters use Amor to gain data-driven insights into technical prowess and work ethic, significantly reducing time-to-hire. The platform helps build a robust pipeline of expert engineers who are proven builders, streamlining the entire hiring pipeline from discovery to outreach.

For Technical Recruiting Agencies

Gain a competitive edge by accessing exclusive talent pools invisible to LinkedIn-centric recruiters. Agencies leverage Amor to find and place top-tier senior engineering talent faster than their competitors. The ability to source from underrepresented communities and untapped GitHub networks allows for a superior and more exclusive candidate offering.

For Engineering Managers

Hire engineers who will genuinely thrive on your team. Engineering managers use Amor to quickly evaluate both technical expertise and cultural fit indicators through verifiable code history. The platform empowers them to raise their team's bar by making informed decisions based on contribution patterns and project involvement, not just resumes.

For Collaborative Hiring Workflows

Accelerate placement decisions through team alignment. Hiring teams can create and share candidate lists, add profile comments for visibility, and collaborate seamlessly within Amor. This shared context reduces back-and-forth communication and speeds up consensus, enabling faster, more coordinated hiring efforts.

How does Amor find candidate contact information?

Amor auto-enriches candidate profiles by extracting email addresses directly from their public GitHub commits where available. The platform also includes discovered LinkedIn URLs and social profiles, providing multiple channels for outreach. All this enriched data is neatly formatted for export.

What makes Amor different from LinkedIn Recruiter?

Amor offers a code-first, data-driven approach focused on verifiable work, while LinkedIn relies on self-reported profiles and networking activity. Amor taps into the vast pool of engineers who are active builders on GitHub but may not be actively networking on LinkedIn, providing access to a different and often more technically focused talent segment.

Can I integrate Amor with my Applicant Tracking System (ATS)?

Yes, Amor offers direct integration capabilities. You can export your candidate lists directly to Ashby in one click using an Ashby-compatible CSV format. The export includes all enriched data, smart tags, and source information for easy organization and import into your ATS.

How accurate is the location data on Amor?

Amor dedicates significant processing to clean noisy GitHub location data. The platform standardizes and validates location information to enable reliable searching by city, region, and country. This provides a much higher degree of accuracy than the raw, user-entered location data found on typical GitHub profiles.